Method and apparatus for detecting high-impedance faults in electrical power systems

ABSTRACT

The present invention features a method and apparatus for detecting and enabling the clearance of high impedance faults (HIFs) in an electrical transmission or distribution system. Current in at least one phase in a distribution system is monitored in real time by sensors. Analog current signature information is then digitized for processing by a digital computer. Zero crossings are identified and current maxima and minima located. The first derivatives of the maxima and minima are computed and a modified Fast Fourier Transform (FFT) is then performed to convert time domain to frequency domain information. The transformed data is formatted and normalized and then applied to a trained neural network, which provides an output trigger signal when an HIF condition is probable. The trigger signal is made available to either a network administrator for manual intervention, or directly to switchgear to deactivate an affected portion of the network. The inventive method may be practiced using either conventional computer hardware and software or dedicated custom hardware such as a VLSI chip.

FIELD OF THE INVENTION

The invention pertains to the detection of faults in an electrical powersystem and more particularly, to the detection of high-impedance faultsin power transmission and distribution systems caused by downedconductors, tree limbs across conductors and the like.

BACKGROUND OF THE INVENTION

In the electrical power generation and distribution industry, monitoringtransmission and distribution networks for fault conditions is veryimportant. The term distribution network is used herein to refer to anyelectrical power transmission or distribution facility. Moreover, afault condition is any abnormal (unexpected) current-conducting path ina distribution network. Fault conditions often present danger to peopleand property. They also waste electrical energy.

One type of fault is a bolted (short-circuit) fault of one or more legsof a distribution network to another leg thereof or to ground. This typeof low-impedance (low-Z) fault condition is easily detected byconventional circuit overcurrent protective devices such as fuses orcircuit breakers. In other words, a complete short circuit (alow-impedance path) usually trips a circuit breaker or blows a fuse. Acircuit of the distribution network experiencing such a fault conditionis quickly removed from service until such time as repairs can beeffected (i.e., until the fault is cleared).

Another type of fault condition occurs when an unintended high impedance(high-Z) conductive path occurs between transmission line legs orbetween one leg and ground. Such high-Z paths may occur when a tree limbor the like falls across a transmission line or when a single leg of atransmission line breaks (due to ice or wind, for example) and touchesthe ground. Generally, a single conductor of the distribution networkdropping to the ground will not create a short circuit, but willcontinue to allow current flow at a relatively low rate. Such currentflow often causes arcing. This condition presents a great danger ofelectrocution to people or animals happening across the downedconductor. The arcing can also result in fires.

A problem constantly plaguing the electrical power industry is findingan effective way to differentiate between a high-Z fault (HIF) conditionand similar effects caused by changes in the loads attached to thedistribution network. In addition to load switching events, power factorcorrecting capacitor banks are frequently switched on and off thenetwork and transformer taps are automatically changed to keep thenetwork voltage constant. Both of these events also create conditions onthe network which may appear similar to an HIF condition. Any effectivesystem for detecting HIFs must be able to distinguish fault conditionsfrom normal load switching events. A system which ignores legitimate HIFconditions risks the aforementioned dangers while a system which falselytrips in response to normal load switching events can wrack havoc withconsumers relying on uninterrupted electrical service. Interruption ofelectrical service to certain manufacturing processes, for example, maydestroy work-in-process and result in large expense to the manufacturer.An interruption of medical apparatus can also be inconvenient at best,and disastrous at worst.

HIF detection solutions as simple as lowering the trip points ofconventional circuit protective devices have been tried. BecauseHIF-drawn current is often a very small percentage of the total networkcurrent, this solution has done nothing more than cause excessiveservice interruptions. Most HIF research has focused upon the problem offinding detectable differences in measurable parameters in adistribution network under HIF and normal load switching conditions.Some of the parameters measured and compared have included phasecurrent, ground current, ratio of ground current to positive sequencecurrent, and frequency spectra--both near line frequency (typically 60Hz.) and at higher harmonic relationships to line frequency.

One system with potential for detecting HIFs is disclosed in U.S. Pat.No. 5,223,795 issued to Frederick K. Blades, titled "Method andApparatus for Detecting Arcing in Electrical Connections by MonitoringHigh Frequency Noise". Blades discloses a system wherein high-frequencynoise caused by arcing is detected and, when measured above apre-programmed threshold level, trips a circuit protective device. Whilethe Blades system is intended for residential branch circuit uses, it isrepresentative of a class of HIF detection strategies relying onhigh-frequency noise for use in power distribution networks. Thesetechniques have not proven effective in the detection of HIFs, sincenoise generated by HIFs varies widely in both spectrum and intensity. Inaddition, capacitor banks, automatically switched on and off the networkfor power factor correction, tend to short the high-frequency noisesignals to ground, adding additional uncertainty to the detection andanalysis process.

Another approach to HIF detection is taught in U.S. Pat. No. 5,216,621issued to Richard T. Dickens, titled "Line Disturbance-Monitor andRecorder System". Dickens discloses a system comprising analog currentand/or voltage sensors placed at selected positions in a distributionnetwork. Analog signals from the detectors are digitized byanalog-to-digital (A/D) converters and presented to a high-speed digitalsignal processor (DSP) as digital sample words. The DSP computes boththe real and imaginary phasor components of the operating parameter orparameters. These phasor components are then used to calculate variousmeasures of power transmission performance according to known phasorequations. Trigger means implemented within the DSP provides an outputsignal when pre-programmed limits of a measured or calculated quantityare exceeded. Memory in cooperation with the DSP captures and storesdigital sample words associated with abnormal events for futureanalysis. The Dickens apparatus appears to be expensive to build and,even with a state-of-the-art DSP, the system may only detect HIFs withwell-known characteristics. Each installation on a network may have tobe individually calibrated to the characteristics of that network and,as the loads changed on the network (e.g., by adding or removing powercustomers), the system would have to be re-calibrated.

A third approach to HIF detection is disclosed in U.S. Pat. No.4,878,142 issued to Sten Bergman, et al., titled "High Resistance GroundFault Protection". Bergman discloses a system for analyzing thenon-harmonic components of phase currents. A estimate of Fouriercoefficients is computed, thereby transforming the time-domaininformation into the frequency domain. Both the original digitizedsignals and the transformed frequency domain signals are applied todetection circuitry. Logical decisions based on comparison to knownfault parameters are made and a fault-indicating trip signal is providedwhen appropriate. The Bergman system suffers from many of the sameshortcomings as the aforementioned Dickens system. The Bergman systemmust be calibrated to each network and re-calibrated when the loadprofile on the network changes.

Yet a fourth HIF detection system is described in "A Neural NetworkApproach to the Detection of Incipient Faults on Power DistributionFeeders", paper No. 89 TD 377-3 PWRD, S. Ebron, D. Lubkemen and M. White(1989). That HIF detection system relies on the monitoring,digitization, and comparison of voltage and current conditions in thedistribution network. The mechanism for deciding whether an event is anHIF or a normal load switching event is a partially or fully-trainedneural network. The 200-node neural network described by Ebron et al. istrained using data obtained from a computer-simulated distributionnetwork using the Electromagnetic Transients Program (EMTP) by SystemsControl, Inc. Data is collected for ten cycles (two subsequentzero-crossings going from negative to positive) of simulated operation.Digitized data is pre-processed to extract features such as peaktransient current over three phases, and phase currents immediatelybefore and after the occurrence of a detected transient. The extractedfeature vector representing ten cycles of simulated network operation isthen applied to a neural network. The network, once at least partiallytrained, then identifies patterns in the data as either HIF or normalload switch events. A trigger signal can be generated when an HIF isdetected.

It is therefore an object of the present invention is to provide amethod and apparatus for monitoring an electrical power distributionnetwork and for distinguishing HIF conditions from normal load,capacitor bank or transformer tap switching conditions with extremelyhigh accuracy.

It is a further object of the invention to produce a systemself-adaptable to a variety of networks and one that need not becalibrated for changes in load on the network.

It is still a further object of the invention to provide aself-contained, low-cost, single-chip hardware implementation of theinventive method for use either as a standalone HIF monitor, or as anintegral part of a circuit-interrupting device for completelyself-contained fault clearing.

SUMMARY OF THE INVENTION

In accordance with the present invention, there is provided a method andapparatus for detecting and clearing high impedance faults (HIFs) in anelectrical transmission or distribution system. Current in at least onephase in a distribution system is monitored in real time by sensors.Analog current/voltage signature information is then digitized forprocessing by a digital computer. Zero crossings are identified andcurrent maxima and minima for each cycle located. The first derivativesof the maxima and minima are computed and a modified Fast FourierTransform (FFT) is then performed to convert these time-domainderivatives to frequency domain information. The transformed data isformatted, summed and normalized and then applied to a trained neuralnetwork, which provides an output trigger signal when an HIF conditionis probable. The trigger signal is made available to either a networkadministrator for manual intervention, or directly to switchgear todeactivate an affected portion of the network. The inventive method maybe practiced using either conventional computer hardware and software ordedicated custom hardware such as a VLSI chip.

BRIEF DESCRIPTION OF THE DRAWINGS

A complete understanding of the present invention may be obtained byreference to the accompanying drawings, when taken in conjunction withthe detailed description thereof and in which:

FIG. 1 is a functional block diagram in accordance with the presentinvention;

FIG. 2 is a flow chart showing the pre-processing steps of the method ofthe present invention;

FIG. 3 is a program listing of the pre-processing commands;

FIGS. 4a and 4b show block diagrams of alternate hardware embodiments ofthe present invention; and

FIGS. 5a and 5b, respectively, show fault detection probability withoutand with first derivative calculation.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Generally speaking, the invention relates to the field of faultdetection in electrical power transmission and distribution systems andnetworks. It should be understood that the terms "power distributionnetwork" or "network" are hereinafter used to refer to any alternatingcurrent electrical transmission or distribution system or facility. Morespecifically, there is disclosed a system for the identification ofhigh-impedance faults (HIFs) which is capable of differentiating withhigh accuracy fault conditions drawing as small a current as 200 mA in apower distribution network. HIF conditions may, however, draw currentsas high as 700 amps. The system is applicable to both single-phase andpoly-phase networks.

Referring now to FIG. 1, there is shown a functional block diagram ofthe present invention. A portion of a typical three-phase powerdistribution network is shown generally at reference numeral 10. Phaseconductors 12a, 12b and 12c of distribution network 10 each aremonitored by current transformers 14a, 14b and 14c, respectively. Sincecurrent transformers are well known in the art, it should be noted thatany current transformer or transducer capable of generating a low-noiseanalog signal representative of the current may be employed. Typically aModel No. MF12540 MF slip-over bushing current transformer manufacturedby Associated Engineering Corp. could be used. The HIF detection systemof the present invention could be used effectively to monitor a singlephase network or a single phase of a poly-phase distribution network.

Analog signals representing phase currents from phase conductors 12a,12b, and 12c monitored by current transformers 14a, 14b and 14c areprovided to inputs of an analog-to-digital (A/D) converter 16. A/Dconverter 16 is a three or more channel device with 12 bit resolution,typically a Model No. DAS-50 manufactured by Keithley Metrobyte.Sampling rate is approximately 5 kHz. Digitized representations ofanalog phase currents are applied from A/D converter 16 to apre-processor 20 via data bus 18. Pre-processor 20 performs severalmathematical and data formatting operations described in more detailhereinbelow.

Pre-processed data is then applied to the input of a trained artificialneural network (ANN) 22. An ANN is a computer model of the parallelismand interconnectedness of the human brain. Connectionist models existwhich have the ability to derive "rules" by analyzing patterns. The ANNdiffers from more conventional expert systems or general artificialintelligence approaches in that the latter require the existence ofwell-defined rules as a prerequisite for effective operation. Thetraining of an ANN is the process whereby the ANN learns to associateinput states to output states by adjusting weights and biases. In thetraining mode, the expected or desired outcome based upon the applieddata is used. If training data covering a broad enough range ofconditions is provided, the ANN eventually self-develops a patternrelating inputs to outputs allowing it to analyze any applied unknowndata. A trained ANN is therefore highly effective at pattern recognitiontasks. That is, pattern recognition is accomplished at extremely highspeed and with a high probability of a correct outcome, based upon theapplied data.

Training the ANN 22 is necessary to establish the "rules" or weights tobe used to classify events occurring on the network as HIFs or normalswitching events. Training is accomplished by providing multiple sets ofknown data representing both HIF and normal network conditions to theANN 22 along with the correct "answers" corresponding to each data set.Data sets my be compiled using computer simulation techniques or mayconsist of actual field-collected data corresponding to both fault andno-fault conditions of distribution networks.

Actual field-collected data and superposition combinations of field datawere used to train the ANN 22 of the present invention. Over 300 sets offield-collected and super position data were used for the training. Atypical training data set consisted of 2860 data points representing 10seconds of sampling time on a real or simulated network. Digitizationfrequency was 5000 samples/second. The training data sets werepre-processed essentially as will be described in detail hereinbelow.The backprapogation of errors method was the learning technique chosento train ANN 22 of the present invention. A learning rate of 0.05 and amomentum (a method of changing weights based on a previous weight) of0.1 were chosen. Once trained (i.e., appropriate weights established),an ANN my be duplicated and distributed and applied in its intendedapplication as a fixed-weight (non-trainable) ANN.

In the preferred embodiment, ANN 22 comprises 386-MATLAB softwareversion 3.5M from Mathworks, Inc. operating on an IBM® compatiblepersonal computer under the Microsoft® DOS operating system version 5.0or greater.

ANN 22 continuously analyzes data from pre-processor 20 and provides anoutput signal 24 which indicates either a normal condition or an HIF.Signal 24 may be used to alert a network administrator (not shown) of apotential HIF by means of indicating/recording devices 25a and/or 25b.Optionally signal 24 may be utilized directly to clear the assumed faultby sending a trip signal to a protective circuit interrupter 25c or 25das is well known in the art.

Refer now also to the flow chart of FIG. 2 and also to the programlisting of FIG. 3, which represents the code applied to ANN 22. Inlearning mode operation, pre-processor 20 first accumulatesapproximately 5000 digitized current data points for each line phase12a, 12b and 12c being monitored. This represents approximately onesecond of network operation, step 50. Cycles are identified by theirzero-crossing points. Zero-crossing points are identified in the datafor each phase conductor 12a, 12b and 12c. This is accomplished bycalculating when the current crosses from negative to positive sign. Themaximum and minimum current for each cycle is determined, step 52. Thefirst derivative (i.e, the rate of change with respect to time) of themaximum and minimum current values is then taken, step 54.

It has been found through experimentation with both simulated and fieldrecorded data that calculating the first derivative is essential to theprocess of accurately detecting HIFs. For purposes of illustrating theimportance of the first derivative in detecting a high-impedance fault,refer now also to FIGS. 5a and 5b. There are shown bar graphs of ANNoutput (relative value or relative HIF probability) vs. time for a 10second period for an actual HIF condition occurring on a single phase ofa 13 kV three-phase transmission line (not shown). For this fault, thephase B leg of the transmission line was dropped onto ice. Series 1 datashows the faulted condition while Series 2 data shows the line under nofault conditions. In FIG. 5a the first derivative has not beencalculated and, as may be seen, there is little difference in therelative value of Series 1 or Series 2 data. In FIG. 5b, identical datais plotted. However, the first derivative has been calculated, andconsequently there is pronounced difference in the output of the ANN 22in response to the HIF (relative value of the Series 1 data).

A Fast Fourier Transform (FFT) is then performed on each of the twofirst derivatives, step 56 (FIG. 2). Next, a power spectrum is computedfor both positive and negative currents, step 58. This is accomplishedby taking the product of the FFT with its complex conjugate. Total poweris calculated by summing the power spectrum for the one second datawindow, step 60. The positive and negative summed powers are thenaveraged, step 62. The averaged power thus calculated is then formattedand scaled for compatibility with the non-linear transfer function ofthe trained ANN 22, step 64. This is accomplished by representing eachone second averaged power as the number of standard deviations from theaverage Z-scaling. The steps are repeated continuously, if additionaldata is processed, step 66, starting again with step 50. Processingterminates, step 68 when all data has been exhausted.

Referring to FIGS. 1, 4a and 4b, a fault indicating output signal 24 isgenerated by trained ANN 22 and provided to signaling/recording or faultclearing devices 25a, 25b, 25c and 25d. These signaling/recording and/orfault clearing devices are well known in the art and may be configuredin any suitable combination. A visual indicator or audible annunciator25a are used to alert operating personnel (not shown) of the presence ofa fault. This fault indication may prompt action to clear the fault bymanually deenergizing the faulted line. A recording device 25b such as adigital fault recorder, well known in the art, may also be attached tomonitor output signal 24. In other installations, a remotely trippablecircuit breaker 25c or other electrically actuatable fault clearingdevice 25d may operate automatically upon receipt of the faultindicating output signal 24 to clear the faulted line.

Alternate embodiments of the present invention may be implementedwherein the entire A/D converter 16, data pre-processor 20 and ANN 22are implemented as a dedicated self-contained device. FIG. 4a showsgenerally, at reference numeral 30, the functions of A/D converter 16(FIG. 1), data pre-processor 20 and ANN 22 all implemented using acommercial microprocessor 32, A/D chip(s) 34 and a commercial EPROM 36customized with all necessary program instructions. FIG. 4b shows anembodiment where A/D converter 16, data pre-processor 20 and ANN 22 areall implemented as a single VLSI chip or equivalent chip shown asreference numeral 40.

In yet another embodiment of the present invention, the VLSI orequivalent chip may be packaged as part of a fault-clearing or circuitprotecting device. Output from the HIF detecting circuit chip would thentrip the circuit protecting device directly.

In still another embodiment of the present invention, input data leadingto an HIF determination by the ANN 22 would be captured and stored. Suchdata would then be used either to further train other ANNs (not shown)or, in cooperation with other analysis software not part of the presentinvention, to create a set of heuristic "rules" in a form usable by moregeneral expert systems. An expert system (e.g., a "fuzzy logic"inference engine) using rules so derived could replace the ANN 22 as theHIF decision-making component of the present invention.

Since other modifications and changes varied to fit particular operatingrequirements and environments will be apparent to those skilled in theart, the invention is not considered limited to the example chosen forpurposes of disclosure, and covers all changes and modifications whichdo not constitute departures from the true spirit and scope of thisinvention.

Having thus described the invention, what is desired to be protected byLetters Patent is presented in the subsequent appended claims.

What is claimed is:
 1. A method for detecting high impedance faults on apower line, the steps comprising:a) sensing a plurality of cycles ofcurrents attributable to a power line and generating a signalrepresentative thereof; b) computing a first derivative ofcycle-to-cycle maxima or minima of said current; c) transforming saidderivatives into a frequency domain representation thereof, the powerspectrum of which frequency domain representation being usable by meansfor processing information; d) applying said power spectrum to means forprocessing information using artificial intelligence techniques todetect a high-impedance fault and to generate a fault signal indicativethereof; and e) actuating a switch in response to said fault signal. 2.The method for detecting high impedance faults on a power line inaccordance with claim 1, wherein said means for processing informationcomprises a neural network.
 3. The method for detecting high impedancefaults on a power line in accordance with claim 2, wherein said neuralnetwork is trained by applying signals representative of line currentsindicative of a high impedance fault.
 4. The method for detecting highimpedance faults on a power line in accordance with claim 3, whereinsaid neural network training is accomplished by the use of simulateddata.
 5. The method for detecting high impedance faults on a power linein accordance with claim 4, wherein said simulation is based onsuperposition of actual data.
 6. The method for detecting high impedancefaults on a power line in accordance with claim 3, wherein said neuralnetwork training is accomplished by applying actual data.
 7. The methodfor detecting high impedance faults on a power line in accordance withclaim 1, wherein said transforming step (c) further comprisesdetermining an envelope based on minimum and maximum signals andprocessing said envelope.
 8. The method for detecting high impedancefaults on a power line in accordance with claim 1, wherein saidactuating step (e) results in operating a circuit clearing device. 9.The method for detecting high impedance faults on a power line inaccordance with claim 8, wherein said clearing device is a clearingdevice disposed along said power line.
 10. The method for detecting highimpedance faults on a power line in accordance with claim 1, whereinsaid actuating step (e) results in operating an indicating device. 11.The method for detecting high impedance faults on a power line inaccordance with claim 1, the steps further comprising:f) prior toapplying said power spectrum to said means for processing information,mathematically conditioning said power spectrum.
 12. The method fordetecting high impedance faults on a power line in accordance with claim11, wherein step (f) comprises the steps of:i) summing positive andnegative power spectra; and ii) averaging said sums.
 13. A method fordetecting high impedance faults on a power line, comprising:a) providinga rules-driven expert system; b) sensing voltages and/or currentsattributable to a line of a power distribution system and generating asignal representative thereof; c) computing a first derivative ofcycle-to-cycle maxima or minima of said voltages and/or currents; d)mathematically preconditioning said signal; e) transforming saidpreconditioned signal representative of said line voltages and/or linecurrents into a frequency domain representation usable by saidrules-driven expert system; f) applying said frequency domainrepresentation to said expert system to detect a high-impedance faultand to generate a fault signal indicative thereof; and g) initiating anevent in response to said fault signal.
 14. The method for detectinghigh impedance faults on a line of a power distribution system inaccordance with claim 13, wherein said rules-driven expert systemcomprises fuzzy logic.
 15. The method for detecting high impedancefaults on a line of a power distribution system in accordance with claim13, wherein said transforming step (e) further comprises determining anenvelope based on minimum and maximum signals and processing saidenvelope.
 16. The method for detecting high impedance faults in a lineof a power distribution system in accordance with claim 13, wherein saidinitiating step (g) results in operating a circuit clearing device. 17.The method for detecting high impedance faults in a line of a powerdistribution system in accordance with claim 13, wherein said initiatingstep (g) comprises the sub-step of recording the data.
 18. The methodfor detecting high-impedance faults in a line of a power distributionsystem of claim 17, wherein said recording sub-step is performed on adigital fault recorder.
 19. The method for detecting high impedancefaults in a line of a power distribution system in accordance with claim16, wherein said clearing device is a clearing device disposed alongsaid line of said power distribution system.
 20. The method fordetecting high impedance faults on a power line in accordance with claim13, wherein said initiating step (g) results in operating an indicatingdevice.
 21. The method for detecting high impedance faults on a powerline in accordance with claim 13, the steps further comprising:h) priorto applying said frequency domain representation to said expert system,mathematically conditioning said frequency domain representation. 22.The method for detecting high impedance faults on a power line inaccordance with claim 21, wherein step (h) comprises the steps of:i)summing positive and negative frequency domain representations; and ii)averaging said sums.
 23. A method for detecting high impedance faults ona power line, comprising:a) sensing a plurality of cycles of currentattributable to a power line and generating a signal representativethereof; b) detecting changes in the cycle-to-cycle positive andnegative peaks of the current waveform; and c) initiating an event onlywhen said changes are detected;wherein the step (b) of detecting changesin the cycle-to-cycle positive and negative peaks of the currentwaveform comprises: i) computing a first derivative of cycle-to-cyclemaxima or minima of said current; ii) transforming said derivatives intoa frequency domain representation thereof, the power spectrum of whichfrequency domain representation being usable by means for processinginformation; and iii) applying said power spectrum to means forprocessing information using artificial intelligence techniques todetect a high-impedance fault and to generate a fault signal indicativethereof.
 24. The method for detecting high impedance faults on a powerline in accordance with claim 23, wherein said transforming substep (ii)of detecting step (b) further comprises determining an envelope based onminimum and maximum signals and processing said envelope.
 25. The methodfor detecting high impedance faults on a power line in accordance withclaim 23, the steps further comprising:d) prior to applying said powerspectrum to said means for processing information, conditioning saidpower spectrum.
 26. The method for detecting high impedance faults on apower line in accordance with claim 25, wherein step (d) comprises thesteps of:i) summing positive and negative power spectra; and ii)averaging said sums.
 27. A method for detecting high impedance faults ona power line, comprising:a) sensing a plurality of cycles of voltageattributable to a power line and generating signal representativethereof; b) detecting changes in the cycle-to-cycle positive andnegative peaks of the voltage waveform; and c) initiating an event onlywhen said changes are detected;wherein the step (b) of detecting changesin the cycle-to-cycle positive and negative peaks of the voltagewaveform comprises: i) computing a first derivative of cycle-to-cyclemaxima or minima of said voltage; ii) transforming said derivatives intoa frequency domain representation thereof, the power spectrum of whichfrequency domain representation being usable by means for processinginformation; and iii) applying said power spectrum to means forprocessing information using artificial intelligence techniques todetect a high-impedance fault and to generate a fault signal indicativethereof.
 28. The method for detecting high impedance faults on a powerline in accordance with claim 27, wherein said transforming substep (ii)of detecting step (b) further comprises determining an envelope based onminimum and maximum signals and processing said envelope.
 29. The methodfor detecting high impedance faults on a power line in accordance withclaim 27, wherein said initiating step (c) results in operating acircuit clearing device.
 30. The method for detecting high impedancefaults on a power line in accordance with claim 27, the steps furthercomprising:d) prior to applying said power spectrum to said means forprocessing information, conditioning said power spectrum.
 31. The methodfor detecting high impedance faults on a power line in accordance withclaim 29, wherein said clearing device is a clearing device disposedalong said power line.
 32. The method for detecting high impedancefaults on a power line in accordance with claim 30, wherein step (d)comprises the steps of:i) summing positive and negative power spectra;and ii) averaging said sums.